In order to convert label masks to corresponding images, we trained a pix2pix model. For the training, a dataset of existing real defect images and manually labeled annotation masks was used. The basic scheme of the training process is depicted in Fig. REF . We used the pix2pix network architecture that was introduced by [1]}. We adapted the size of the input layer to fit the dimensions of our dataset. Other than that, we did not make any modifications to the originally proposed architecture.
